Investigating the Vehicle Routing Problem with Simultaneous Pickup and Delivery in Multi-Product Distribution: An Optimization Approach

This study addresses a method to solve the Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD), which carries multi-products in multiple compartments within a single-vehicle. The unique characteristics of the study is on the route determination of the vehicle from the depot to customers because not only does it consider the vehicle’s capacity but also the compartment capacity of each product as a limitation We calculate the set of instances using two customer grouping methods namely smallest maximum load (SML) and largest maximum load (LML). The solution obtained by the cheapest insertion method can be improved by the Tabu Search algorithm. Finally, the computational result is reported from the test instance

The location-routing problem with multi-compartment and multi-trip: formulation and heuristic approaches

The location-routing problem with multi-compartment and multi-trip is an extension to the standard location-routing problem. In this problem, depots are used to deliver different products using heterogeneous vehicles with several compartments. Each compartment has a limited capacity and is dedicated to a single type of product. The problem is formulated as a mixed integer program. A constructive heuristic and a hybrid genetic algorithm (HGA) are proposed. Numerical experiments show that both heuristics can efficiently determine the optimal solutions on small size instances. For larger ones, the HGA outperforms the constructive heuristic with relatively more computational time. Managerial insights have been obtained from sensitivity analyses which would be helpful to improve the performance of the supply network

Applications of the Internet of Things and optimization to inventory and distribution management

This thesis is part of the IoFEED (EU funded) project, which aims to monitor approximately 325 farm bins and investigates business processes carried out between farmers and animal feed producers. We propose a computer-aided system to control and optimize the supply chain to deliver animal feed to livestock farms. Orders can be of multiple types of feed, shipped from multiple depots using a fleet of heterogeneous vehicles with multiple compartments. Additionally, this case considers some business-specific constraints, such as product compatibility, facility accessibility restrictions, prioritized locations, or bio-security constraints. A digital twin based approach is implemented at the farm level by installing sensors to remotely measure the inventories. This thesis also embraces these sensors' design and manufacturing process, seeking the required precision and easy deployability at scale. Our approach combines biased-randomization techniques with a simheuristic framework to make use of data provided by the sensors. The analysis of results is based on these two real pilots, and showcases the insights obtained during the IoFEED project. The results of this thesis show how the Internet of Things and simulation-based optimization methods combine successfully to optimize deliveries of feed to livestock farms.Esta tesis forma parte del proyecto IoFeeD, financiado por la Unión Europea, que tiene como objetivo monitorizar remotamente el stock de 325 contenedores agrícolas e investigar los procesos comerciales llevados a cabo entre agricultores y productores de pienso. Proponemos un sistema de ayuda a la toma de decisiones para controlar y optimizar la cadena de suministro de pienso en las explotaciones ganaderas. Los pedidos pueden ser de varios tipos de pienso y pueden enviarse desde varios centros de fabricación mediante el uso de una flota de vehículos heterogéneos con varios compartimentos. Además, se tienen en cuenta algunas restricciones específicas de la empresa, como, por ejemplo, la compatibilidad del producto, las restricciones de accesibilidad en las instalaciones, las ubicaciones priorizadas o las restricciones de bioseguridad. A escala de granja, se implementa un enfoque basado en gemelos digitales mediante la instalación de sensores para medir los inventarios de forma remota. En el marco de esta tesis, se desarrollan estos sensores buscando la precisión requerida, así como las características oportunas que permitan su instalación a gran escala. Nuestro enfoque combina técnicas de aleatorización sesgada con un marco simheurístico para hacer uso de los datos proporcionados por los sensores. El análisis de los resultados se basa en estos dos pilotos reales y muestra las ideas obtenidas durante el proyecto IoFeeD. Los resultados de esta tesis muestran cómo la internet de las cosas y los métodos de optimización basados en simulación se combinan con éxito para optimizar las operaciones de suministro de pienso para el consumo animal en las explotaciones ganaderas.Aquesta tesi forma part del projecte IoFeeD, finançat per la Unió Europea, que té com a objectiu controlar remotament l'estoc de 325 sitges i investigar els processos de negoci duts a terme entre agricultors i productors de pinso. Proposem un sistema d'ajuda a la presa de decisions per controlar i optimitzar la cadena de subministrament de pinso a les explotacions ramaderes. Les comandes poden ser de diversos tipus de pinso i es poden enviar des de diversos centres de fabricació mitjançant l'ús d'una flota de vehicles heterogenis amb diversos compartiments. A més, es tenen en compte algunes restriccions específiques de l'empresa, com ara la compatibilitat del producte, les restriccions d'accessibilitat a les instal·lacions, les ubicacions prioritzades o les restriccions de bioseguretat. A escala de granja, s'implementa un enfocament basat en bessons digitals mitjançant la instal·lació de sensors per mesurar remotament els inventaris. En el marc de la tesi, es desenvolupa aquest sensor cercant la precisió requerida i les característiques oportunes que en permetin la instal·lació a gran escala. El nostre enfocament combina tècniques d'aleatorització esbiaixada amb un marc simheurístic per fer ús de les dades proporcionades pels sensors. L'anàlisi dels resultats es basa en aquests dos pilots reals i mostra les idees obtingudes durant el projecte IoFeeD. Els resultats d'aquesta tesi mostren com la internet de les coses i els mètodes d'optimització basats en simulació es combinen amb èxit per optimitzar les operacions de subministrament de pinso per al consum animal a les explotacions ramaderes.Tecnologies de la informació i de xarxe

Optimization and Allocation in Some Decision Problems with Several Agents or with Stochastic Elements

Programa Oficial de Doutoramento en Estatística e Investigación Operativa. 5017V01[Abstract] This dissertation addresses sorne decision problems that arise in project management, cooperative game theory and vehicle route optimization. We start with the problem of allocating the delay costs of a project. In a stochastic context in which we assume that activity durations are random variables, we propose and study an allocation rule based on the Shapley value. In addition, we present an R package that allows a comprehensive control of the project, including the new rule. We propose and characterize new egalitarian solutions in the context of cooperative games with a coalitional structure. Also, using a necessary player property we introduce a new value for cooperative games, which we later extend and characterize within the framework of cooperative games with a coalitional structure. Finally, we present a two-step algorithm for solving multi-compartment vehicle route problems with stochastic demands. This algorithm obtains an initial solution through a constructive heuristic and then uses a tabu search to improve the solution. Using real data, we evaluate the performance of the algorithm.[Resumo] Nesta memoria abórdanse diversos problemas de decisión que xorden na xestión de proxectos, na teoría de xogos cooperativos e na optimización de rutas de vehículos. Empezamos estudando o problema da repartición dos custos de demora nun proxecto. Nun contexto estocástico no que supoñemos que as duracións das actividades son variables aleatorias, propoñemos e estudamos unha regra de repartición baseada no valor de Shapley. Ademais, presentamos un paquete de R que permite un control integral do proxecto, incluíndo a nova regra de repartición. A continuación, propoñemos e caracterizamos axiomaticamente novas solucións igualitarias no contexto dos xogos cooperativos cunha estrutura coalicional. E introducimos un novo valor, utilizando unha propiedade de xogadores necesarios, para xogos cooperativos, que posteriormente estendemos e caracterizamos dentro do marco dos xogos cooperativos cunha estrutura coalicional. Por último, presentamos un algoritmo en dous pasos para resolver problemas de rutas de vehículos con multi-compartimentos e demandas estocásticas. Este algoritmo obtén unha solución inicial mediante unha heurística construtiva e, a continuación, utiliza unha búsqueda tabú para mellorar a solución. Utilizando datos reais, levamos a cabo unha análise do comportamento do algoritmo.[Resumen] En esta memoria se abordan diversos problemas de decisión que surgen en la gestión de proyectos, en la teoría de juegos cooperativos y en la optimización de rutas de vehículos. Empezamos estudiando el problema del reparto de los costes de demora en un proyecto. En un contexto estocástico en el que suponemos que las duraciones de las actividades son variables aleatorias, proponemos y estudiamos una regla de reparto basada en el valor de Shapley. Además, presentamos un paquete de R que permite un control integral del proyecto, incluyendo la nueva regla de reparto. A continuación, proponemos y caracterizamos axiomáticamente nuevas soluciones igualitarias en el contexto de los juegos cooperativos con una estructura coalicional. E introducimos un nuevo valor, utilizando una propiedad de jugadores necesarios, para juegos cooperativos, que posteriormente extendemos y caracterizamos dentro del marco de los juegos cooperativos con una estructura coalicional. Por último, presentamos un algoritmo en dos pasos para resolver problemas de rutas de vehículos con multi-compartimentos y demandas estocásticas. Este algoritmo obtiene una solución inicial mediante una heurística constructiva y, a continuación, utiliza una búsqueda tabú para mejorar la solución. Utilizando datos reales, llevamos a cabo un análisis del comportamiento del algoritmo

Modelling of interactions between rail service and travel demand: a passenger-oriented analysis

The proposed research is situated in the field of design, management and optimisation in railway network operations. Rail transport has in its favour several specific features which make it a key factor in public transport management, above all in high-density contexts. Indeed, such a system is environmentally friendly (reduced pollutant emissions), high-performing (high travel speeds and low values of headways), competitive (low unitary costs per seat-km or carried passenger-km) and presents a high degree of adaptability to intermodality. However, it manifests high vulnerability in the case of breakdowns. This occurs because a faulty convoy cannot be easily overtaken and, sometimes, cannot be easily removed from the line, especially in the case of isolated systems (i.e. systems which are not integrated into an effective network) or when a breakdown occurs on open tracks. Thus, re-establishing ordinary operational conditions may require excessive amounts of time and, as a consequence, an inevitable increase in inconvenience (user generalised cost) for passengers, who might decide to abandon the system or, if already on board, to exclude the railway system from their choice set for the future. It follows that developing appropriate techniques and decision support tools for optimising rail system management, both in ordinary and disruption conditions, would consent a clear influence of the modal split in favour of public transport and, therefore, encourage an important reduction in the externalities caused by the use of private transport, such as air and noise pollution, traffic congestion and accidents, bringing clear benefits to the quality of life for both transport users and non-users (i.e. individuals who are not system users). Managing to model such a complex context, based on numerous interactions among the various components (i.e. infrastructure, signalling system, rolling stock and timetables) is no mean feat. Moreover, in many cases, a fundamental element, which is the inclusion of the modelling of travel demand features in the simulation of railway operations, is neglected. Railway transport, just as any other transport system, is not finalised to itself, but its task is to move people or goods around, and, therefore, a realistic and accurate cost-benefit analysis cannot ignore involved flows features. In particular, considering travel demand into the analysis framework presents a two-sided effect. Primarily, it leads to introduce elements such as convoy capacity constraints and the assessment of dwell times as flow-dependent factors which make the simulation as close as possible to the reality. Specifically, the former allows to take into account the eventuality that not all passengers can board the first arriving train, but only a part of them, due to overcrowded conditions, with a consequent increase in waiting times. Due consideration of this factor is fundamental because, if it were to be repeated, it would make a further contribution to passengers’ discontent. While, as regards the estimate of dwell times on the basis of flows, it becomes fundamental in the planning phase. In fact, estimating dwell times as fixed values, ideally equal for all runs and all stations, can induce differences between actual and planned operations, with a subsequent deterioration in system performance. Thus, neglecting these aspects, above all in crowded contexts, would render the simulation distorted, both in terms of costs and benefits. The second aspect, on the other hand, concerns the correct assessment of effects of the strategies put in place, both in planning phases (strategic decisions such as the realisation of a new infrastructure, the improvement of the current signalling system or the purchasing of new rolling stock) and in operational phases (operational decisions such as the definition of intervention strategies for addressing disruption conditions). In fact, in the management of failures, to date, there are operational procedures which are based on hypothetical times for re-establishing ordinary conditions, estimated by the train driver or by the staff of the operation centre, who, generally, tend to minimise the impact exclusively from the company’s point of view (minimisation of operational costs), rather than from the standpoint of passengers. Additionally, in the definition of intervention strategies, passenger flow and its variation in time (different temporal intervals) and space (different points in the railway network) are rarely considered. It appears obvious, therefore, how the proposed re-examination of the dispatching and rescheduling tasks in a passenger-orientated perspective, should be accompanied by the development of estimation and forecasting techniques for travel demand, aimed at correctly taking into account the peculiarities of the railway system; as well as by the generation of ad-hoc tools designed to simulate the behaviour of passengers in the various phases of the trip (turnstile access, transfer from the turnstiles to the platform, waiting on platform, boarding and alighting process, etc.). The latest workstream in this present study concerns the analysis of the energy problems associated to rail transport. This is closely linked to what has so far been described. Indeed, in order to implement proper energy saving policies, it is, above all, necessary to obtain a reliable estimate of the involved operational times (recovery times, inversion times, buffer times, etc.). Moreover, as the adoption of eco-driving strategies generates an increase in passenger travel times, with everything that this involves, it is important to investigate the trade-off between energy efficiency and increase in user generalised costs. Within this framework, the present study aims at providing a DSS (Decision Support System) for all phases of planning and management of rail transport systems, from that of timetabling to dispatching and rescheduling, also considering space-time travel demand variability as well as the definition of suitable energy-saving policies, by adopting a passenger-orientated perspective